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. 2026 Jan 24:S0012-3692(26)00125-X.
doi: 10.1016/j.chest.2026.01.009. Online ahead of print.

Interactive Pathways of Key Prognostic Factors in Severe Asthma: A Bayesian Network Comparison of Clinical Trials & Real-World Data

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Free article

Interactive Pathways of Key Prognostic Factors in Severe Asthma: A Bayesian Network Comparison of Clinical Trials & Real-World Data

Chandra Prakash Yadav et al. Chest. .
Free article

Abstract

Background: The way in which risk predictors combine and contribute to severe asthma exacerbations may differ between clinical trials and real-world settings.

Research question: How do the interactive pathways of risk predictors leading to severe asthma exacerbations compare under clinical trials versus real-world settings?

Study design: and Methods: The analysis involved 345 severe asthma patients from the placebo arms of two international randomized control trails (RCTs), compared to 6814 biologic-naïve patients from the International Severe Asthma Registry (ISAR). Seventeen key risk predictors including demographics, biomarkers, lung function, healthcare use, exacerbation history, long-term oral corticosteroid use, asthma control, and nasal polyps were covered. The outcome was the occurrence of severe asthma exacerbations over 365-day following study enrolment. Bayesian Networks (BNs), obtained from machine learning combined with expert knowledge, elucidated significant interplay processes of risk predictors that led to severe asthma exacerbations. External validation was performed in each other cohort respectively.

Results: RCTs revealed 44 significant arcs (i.e., probabilistic inter-dependency) between 17 risk factors, while ISAR showed 170. Despite this difference, the main downstream prediction pathways were consistent across both settings, with two key pathways: total serum immunoglobulin E level influenced blood eosinophils to predict future severe exacerbations, and severe exacerbation history directly predicted future severe exacerbations. In external validation, RCTs -BN generalized well to ISAR patients (AUC = 0.68) whereas ISAR-BN underperformed in RCT patients (AUC = 0.50), while ISAR-BN demonstrated better calibration.

Interpretation: The core pathways predicting severe asthma exacerbations were similar in both RCTs and real-world settings, with comparable predictive performance.

Keywords: Evidence-Based Healthcare; Machine Learning; Respiratory Diseases; Risk prediction.

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